Soils and Foundations最新文献

{"title":"Crack evolution mechanism of stratified rock mass under different strength ratios and soft layer thickness: Insights from DEM modeling","authors":"Qinji Jia,&nbsp;Xiaoming Liu,&nbsp;Xin Tan","doi":"10.1016/j.sandf.2024.101534","DOIUrl":"10.1016/j.sandf.2024.101534","url":null,"abstract":"<div><div>Research on stratified rock masses, which are common geological formations, has primarily focused on their mechanical properties, while studies on crack evolution and microscopic damage mechanisms remain limited. This study addresses this gap by investigating the combined effects of strength ratios and soft layer thicknesses on the microcrack evolution mechanism of stratified rocks using the discrete element method (DEM). Through FISH language programming in the particle flow code (PFC), this study reveals the acoustic emission (AE) characteristics, crack initiation and propagation, damage degree, and final failure characteristics. The key findings are: (1) Higher strength ratios between the hard and soft components of stratified rocks make specimens more sensitive to increases in soft layer thickness. (2) Three types of AE events were identified: continuous active, intermittent active, and silent. (3) Cracks initiate at the interface between components and propagate along the interface into the rock matrix. The strength ratios determine the crack propagation path and the damage extent of the components. (4) The failure of stratified rocks is primarily controlled by the soft component. Crack connections typically form vertical and sub-vertical tensile failure planes in the hard component, and a shear failure surface with a “V”-shaped intersection in the soft component.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101534"},"PeriodicalIF":3.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural soils’ shear strength prediction: A morphological data-centric approach","authors":"Maher Omar ,&nbsp;Mohamed G. Arab ,&nbsp;Emran Alotaibi ,&nbsp;Khalid A. Alshibli ,&nbsp;Abdallah Shanableh ,&nbsp;Hussein Elmehdi ,&nbsp;Dima A. Hussien Malkawi ,&nbsp;Ali Tahmaz","doi":"10.1016/j.sandf.2024.101527","DOIUrl":"10.1016/j.sandf.2024.101527","url":null,"abstract":"<div><div>The deformation characteristics and constitutive behavior of granular materials under normal forces acting on particles are dependent on the geometry of the grain structure, fabrics and the inter-particle friction. In this study, the influence of particle morphology on the friction and dilatancy of five natural sands was investigated using deep learning (DL) techniques. A Three-dimensional (3D) imaging technique using computed tomography was utilized to compute the morphology (roundness and sphericity) of collected natural sands. Triaxial tests were conducted on the five different natural sands at different densities and confinement stresses (<em>σ₃</em>). From the triaxial results, peak friction angle (<span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub><mrow><mo>)</mo></mrow></mrow></math></span>, critical state friction angle (<span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span>), and dilatancy angle (ψ) were obtained and modeled using conventional multiple linear regression (MLR) models and DL techniques. A total of 100 deep artificial neural networks (DANN) models were trained at different sizes of first and second hidden layers. The use of MLR resulted in R² of 0.709, 0.565, and 0.795 for <span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub></mrow></math></span>, <span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span> and <em>ψ</em>, respectively, while the best performed DANN (30 and 50 neurons for the 1st and 2nd hidden layers, respectively) had R² of 0.956 for all outputs (<span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub></mrow></math></span>, <span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span> and <em>ψ</em>) combined. Using the best-performed DANN model, the weight partitioning technique was used to compute an importance score for each parameter in predicting <span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub></mrow></math></span>, <span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span> and <em>ψ</em>. The <em>σ₃</em> had the highest importance followed by relative density, roundness, and sphericity with a relative importance of more than 10%. In addition, sensitivity analysis was conducted to investigate the effect of each parameter on the shear parameters and ensure the robustness of the developed model.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101527"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating S-wave velocity profiles from horizontal-to-vertical spectral ratios based on deep learning","authors":"Koichi Hayashi ,&nbsp;Toru Suzuki ,&nbsp;Tomio Inazaki ,&nbsp;Chisato Konishi ,&nbsp;Haruhiko Suzuki ,&nbsp;Hisanori Matsuyama","doi":"10.1016/j.sandf.2024.101525","DOIUrl":"10.1016/j.sandf.2024.101525","url":null,"abstract":"<div><div>S-wave velocity (Vs) profile or time averaged Vs to 30 m depth (V_S30) is indispensable information to estimate the local site amplification of ground motion from earthquakes. We use a horizontal-to-vertical spectral ratio (H/V) of seismic ambient noise to estimate the Vs profiles or V_S30. The measurement of H/V is easier, compared to active surface wave methods (MASW) or microtremor array measurements (MAM). The inversion of H/V is non-unique and it is impossible to obtain unique Vs profiles. We apply deep learning to estimate the Vs profile from H/V together with other information including site coordinates, deep bedrock depths, and geomorphological classification. The pairs of H/V spectra (input layer) and Vs profiles (output layer) are used as training data. An input layer consists of an observed H/V spectrum, site coordinates, deep bedrock depths, and geomorphological classification, and an output layer is a velocity profile. We applied the method to the South Kanto Plain, Japan. We measured MASW, MAM and H/V at approximately 2300 sites. The pairs of H/V spectrum together with their coordinates, geomorphological classification etc. and Vs profile obtained from the inversion of dispersion curve and H/V, compose the training data. A trained neural network predicts Vs profiles from the observed H/V spectra with other information. Predicted Vs profiles and their V_S30 are reasonably consistent with true Vs profiles and their V_S30. The results implied that the deep learning could estimate Vs profile from H/V together with other information.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101525"},"PeriodicalIF":3.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shear band analysis of silt-clay transition soils under three-dimensional stress-strain conditions","authors":"Pongpipat Anantanasakul ,&nbsp;Victor N. Kaliakin","doi":"10.1016/j.sandf.2024.101532","DOIUrl":"10.1016/j.sandf.2024.101532","url":null,"abstract":"<div><div>This paper investigates shear banding as a possible failure mode for silt–clay transition soils under general three-dimensional stress conditions. Drained and undrained true triaxial tests with constant <span><math><mi>b</mi></math></span> values were performed on tall prismatic specimens of such soils with systematically varying silt contents. Based on the values of critical plastic hardening modulus, shear banding does not govern the strength characteristics of the soils for <span><math><mi>b</mi></math></span> values less than 0.2. For larger <span><math><mi>b</mi></math></span> values, shear band formation is essentially critical as it takes place in the hardening regime of the stress–strain curves prior to the smooth peak failure points. An increase in silt content appears to move the onset of shear banding to lower levels of shear in the stress–strain relations of the silt–clay transition soils. It is also demonstrated that a non-associated constitutive model with a single hardening law is capable of accurately predicting the onset of shear banding in normally consolidated silt–clay transition soils based on bifurcation theory.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101532"},"PeriodicalIF":3.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic passive earth pressures of narrow cohesive backfill against gravity walls using the stress characteristics method","authors":"Zhan-liang Wang ,&nbsp;Wu-zhen Kang ,&nbsp;Fu-quan Chen ,&nbsp;Cheng Lin","doi":"10.1016/j.sandf.2024.101505","DOIUrl":"10.1016/j.sandf.2024.101505","url":null,"abstract":"<div><div>A solution method for the determination of seismic passive earth pressures in narrow cohesive backfill behind gravity walls has been developed using the stress characteristics method. The stress characteristics method is combined with the pseudo-static method in the analysis to consider the effects of seismic forces. The failure mechanisms of backfill are complex when the backfill reaches its passive limit state. The stress characteristics method does not require pre-assumptions about the sliding surface and the plastic region of the backfill. This method automatically calculates the position of the sliding surface. The reliability and reasonableness of the proposed method are verified by comparing the sliding surface and seismic passive earth pressure calculated in this paper with the finite element calculation results, the existing experimental research results and the existing theoretical solution results. The effect of different parameters on seismic passive earth pressure is investigated by internal stress clouds of the backfill and the distribution of passive earth pressure on the retaining wall.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101505"},"PeriodicalIF":3.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study and correction of dynamic characteristic parameters of silty clay under negative temperature conditions","authors":"Haotian Guo ,&nbsp;Yuli Lin ,&nbsp;Jinming Li ,&nbsp;Chao Sun","doi":"10.1016/j.sandf.2024.101530","DOIUrl":"10.1016/j.sandf.2024.101530","url":null,"abstract":"<div><div>In order to examine the principles governing the variation of dynamic characteristic parameters, including the damping ratio, dynamic modulus, and frozen soil backbone curve, under different negative temperature conditions, silty clays sourced from the Changchun region were selected for the research. Dynamic loading studies were carried out on silty clays under different negative temperature conditions using a temperature-controlled GDS dynamic triaxial machine. The results demonstrated that the lower the temperature, the higher the dynamic stress required to achieve the same dynamic strain. The inverse of the dynamic modulus <span><math><mrow><mn>1</mn><mo>/</mo><msub><mi>E</mi><mi>d</mi></msub></mrow></math></span> is linearly related to the dynamic strain, and the intercept of the fitted line of the inverse of <span><math><mrow><mn>1</mn><mo>/</mo><msub><mi>E</mi><mi>d</mi></msub></mrow></math></span> decreases with decreasing temperature. The damping ratio and ability to absorb vibration waves decrease as the temperature drops. As the temperature decreases, the maximum dynamic modulus gradually increases, and the maximum damping ratio has the opposite trend. The temperature correction formulas for the maximum dynamic modulus and maximum damping ratio of silty clay are proposed by correlation analysis method based on test data.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101530"},"PeriodicalIF":3.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of suffusion on small strain shear modulus of gap-graded soil under principal stress rotation","authors":"Sanjei Chitravel ,&nbsp;Masahide Otsubo ,&nbsp;Reiko Kuwano","doi":"10.1016/j.sandf.2024.101518","DOIUrl":"10.1016/j.sandf.2024.101518","url":null,"abstract":"<div><div>Internal erosion involves the transport of soil particles from within or beneath a geotechnical structure due to seepage flow, influencing the subsequent mechanical and hydraulic behaviour of the soil. However, predicting changes in small-strain modulus (<span><math><mrow><msub><mi>G</mi><mrow><mi>max</mi></mrow></msub></mrow></math></span>) with eroded fines and varying principal stress directions can be challenging due to various factors related to soil fabric. The present study investigates the impact of seepage flow on <span><math><mrow><msub><mi>G</mi><mrow><mi>max</mi></mrow></msub></mrow></math></span>, as well as the effect of principal stress rotation (PSR), of gap-graded soil with a fines content of 20%, using a novel erosion hollow cylindrical torsion shear apparatus. The erosion test results indicate that, regardless of density, the <span><math><mrow><msub><mi>G</mi><mrow><mi>max</mi></mrow></msub></mrow></math></span> generally increases with seepage time. The trend of <span><math><mrow><msub><mi>G</mi><mrow><mi>max</mi></mrow></msub></mrow></math></span> measured in the vertical and torsional directions varies significantly, as seepage is applied always downward, resulting in a different impact on the vertical and horizontal bedding planes. After a cycle of PSR, the induced torsional shear strain is found larger for the eroded specimens, while vertical strain decreases due to fine removal accompanied by seepage flow. In the PSR tests, the specimens subjected to erosion exhibit a greater reduction in <span><math><mrow><mspace></mspace><msub><mi>G</mi><mrow><mi>max</mi></mrow></msub></mrow></math></span> compared to non-eroded specimens, with increasing the angles of principal stress direction. This reduction may be due to the inefficacy of the reinforced soil skeleton established by erosion against shearing. The distribution of fine particles and anisotropy induced by seepage flow contribute to non-trivial mechanical behaviour during principal stress rotation, particularly regarding small-strain shear modulus.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101518"},"PeriodicalIF":3.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review and comparison of machine learning methods in developing optimal models for predicting geotechnical properties with consideration of feature selection","authors":"Tengyuan Zhao ,&nbsp;Fenglin Shen ,&nbsp;Ling Xu","doi":"10.1016/j.sandf.2024.101523","DOIUrl":"10.1016/j.sandf.2024.101523","url":null,"abstract":"<div><div>Geotechnical properties, such as cohesion, pile drivability, rock strength, is one of the most important and indispensable input for design or analysis of geotechnical/geological engineering projects. Conventionally, these properties are obtained from laboratory experiments with well-prepared samples or well-designed experiments in-situ. Although direct measurements are generally accurate, they are often time-consuming and laborious, and acquisition of numerous measurements is often not available. This is especially true for medium- or small-sized projects. Alternatively, the properties of interest can be predicted from readily available indices by some machine learning (ML) methods, which has been applied to geotechnical engineering increasingly in recent years. Although ML methods perform reasonably well in predicting target geotechnical properties, all features considered subjectively relevant were often taken as input to the developed model. However, not all features contribute equally significant to the prediction. Involvement of irrelevant indices in an ML model would increase the model complexity, add additional difficulty in result interpretation, and introduce a risk of degrading the model’s generalization ability. Although these points have been well recognized in literature, only few studies carried out feature selection when ML methods are applied to geotechnical/geological engineering. This paper aims to alleviate this gap by offering a comprehensive review and comparison of commonly used ML methods, with consideration of various methods for feature selection. Selection of relevant features for the problem at hand also agrees well with the spirit of “<em>data first practice central agenda</em>” in data-centric geotechnics. Both simulated and real-life datasets are used to compare performance of the various ML methods in feature selection and prediction. Results show that fully Bayesian-Gaussian process regression (fB-GPR) outperforms other ML models.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101523"},"PeriodicalIF":3.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundation studies with physical modeling","authors":"Osamu Kusakabe","doi":"10.1016/j.sandf.2024.101521","DOIUrl":"10.1016/j.sandf.2024.101521","url":null,"abstract":"<div><div>This contribution is part of a series of invited papers on “A Review of the Author’s Own Seminal Contributions”. The paper describes the author’s 45 years of research experiences with a focus on foundation studies with physical modeling. Following some general statements on physical modeling, the facilities that the author utilized are described; and subsequently, the selected foundation problems that he tackled are explained mainly from physical modeling viewpoints. The selected problems cover shallow/ deep foundation stability problems and a few geoenvironmental issues, such as ground vibrations and piling at post-closure waste disposal sites. The outcomes of his research offered engineering solutions that society needs. The paper emphasizes the usefulness of the methodology of combining the theory of plasticity and physical modeling.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101521"},"PeriodicalIF":3.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognizing gradations of coarse soils based on big artificial samples and deep learning","authors":"Yuan-en Pang ,&nbsp;Xu Li ,&nbsp;Zu-yu Chen","doi":"10.1016/j.sandf.2024.101526","DOIUrl":"10.1016/j.sandf.2024.101526","url":null,"abstract":"<div><div>In earth-rockfill dams, roadbeds, airports, and other embankment projects, gradation information serves as the basis for evaluating the quality and suitability of fill materials. Addressing the limitations of existing image-based contour recognition methods and machine learning approaches in recognizing small particle size ranges, this study establishes the first publicly available coarse-grained soil database including Yellow River Silt and Quartz Sand datasets, with particle sizes ranging from 0.075 to 20 mm, comprising a total of 22,380 images. Subsequently, a novel Convolutional Neural Network (CNN) architecture, the Searcher-Analyzer Network (SaNet), based on the Deep Residual Network (ResNet), was proposed to enhance the accuracy of gradation recognition by taking multiple images under a single gradation as input. Finally, the interpretability of the model was discussed through feature map visualization. The results demonstrate that SaNet achieves <span><math><mrow><mover><mrow><mrow><mi>MAE</mi></mrow></mrow><mrow><mo>¯</mo></mrow></mover></mrow></math></span> of 1.63 × 10⁻² and <span><math><mrow><mover><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow><mrow><mo>¯</mo></mrow></mover></mrow></math></span> of 0.995 for Yellow River Silt, and <span><math><mrow><mover><mrow><mrow><mi>MAE</mi></mrow></mrow><mrow><mo>¯</mo></mrow></mover></mrow></math></span> of<!--> <!-->1.21 × 10⁻² and <span><math><mrow><mover><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow><mrow><mo>¯</mo></mrow></mover></mrow></math></span> of 0.992 for Quartz Sand. Concurrently, the additional computational time and storage requirements are only 3.5 % and 0.3 % more than those of ResNet, allowing the recognition of a single image to be completed within 10 ms. The findings of this study indicate that the proposed SaNet model can instantly achieve high accuracy in gradation recognition, meeting the demands for real-time, non-destructive gradation testing in related tasks.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101526"},"PeriodicalIF":3.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}